文章目录
创建 root
sudo passwd root
su root
参考博客
https://blog.csdn.net/weixin_38661447/article/details/106796349
华为镜像元 配置
sudo cp -a /etc/apt/sources.list /etc/apt/sources.list.bak
sudo sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
sudo sed -i "s@http://.*security.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
apt-get update
更新 vim 防止 方向键 乱码
关闭 nouveau
vim /etc/modprobe.d/blacklist.conf
sudo echo " blacklist nouveau " >> /etc/modprobe.d/blacklist.conf
blacklist nouveau
update-initramfs -u
检查
lsmod | grep nouveau
//无输出表示成功
安装 gpu 驱动
10.2 440
参考
https://blog.csdn.net/qq_43373608/article/details/103314435
添加驱动源
add-apt-repository ppa:graphics-drivers/ppa
apt-get update
apt install nvidia-driver-440 -y
安装 cuda10.2
wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
sh cuda_10.2.89_440.33.01_linux.run
配置环境变量
vi ~/.bashrc
export PATH="/usr/local/cuda-10.2/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH"
下载 https://developer.nvidia.com/cudnn
安装runtime库
dpkg -i '/home/zhao/下载/libcudnn8_8.0.3.33-1+cuda10.2_amd64.deb'
安装developer库
dpkg -i '/home/zhao/下载/libcudnn8-dev_8.0.3.33-1+cuda10.2_amd64.deb'
安装代码示例和《cuDNN库用户指南》
dpkg -i '/home/zhao/下载/libcudnn8-samples_8.0.3.33-1+cuda10.2_amd64.deb'
安装 docker nvidiaruntime
apt-get install apt-transport-https ca-certificates curl gnupg-agent software-properties-common -y
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
apt-key fingerprint 0EBFCD88
add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
apt-get update
apt-get install docker-ce -y
systemctl enable docker
systemctl start docker
docker run hello-world
取消 docker root 权限
sudo groupadd docker
sudo gpasswd -a $USER docker #将登陆用户加入到docker用户组中
newgrp docker #更新用户组
安装 nvidia-container-toolkit
#Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
apt-get update && sudo apt-get install -y nvidia-container-toolkit
systemctl restart docker
docker gpu 镜像
https://tensorflow.google.cn/install/docker
https://www.cnblogs.com/g2thend/p/12256018.html
docker pull tensorflow/tensorflow:latest-gpu-jupyter
docker run --gpus all --rm nvidia/cuda nvidia-smi
docker run --gpus all -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter
//挂载目录之前 先把 目录 权限打开
docker run --gpus all -p 8888:8888 --name user1 --privileged=true -e PASSWORD=your_jupyter_passwd -v 公共目录:/home/x -v 私有目录:/home/y tensorflow/tensorflow:latest-gpu-jupyter
docker run --gpus all -p 8888:8888 --name user1 --privileged=true -e PASSWORD=your_jupyter_passwd -v 公共目录:/home/x -v 私有目录:/home/y tensorflow/tensorflow:latest-gpu-jupyter
docker run --gpus all -p 8888:8888 --name user1 --privileged=true -e PASSWORD=your_jupyter_passwd -v 公共目录:/home/x -v 私有目录:/home/y tensorflow/tensorflow:latest-gpu-jupyter
docker run -d --gpus all -p 18888:8888 --name llhtfgpu23 --privileged=true -v /home/zhao/students:/tf/public -v /home/zhao/llh:/tf/myself tensorflow/tensorflow:latest-gpu-jupyter
docker logs llhtfgpu23
备份系统
sudo root
cd /
todayDate=$(date +'%Y%m%d')
tar -cvpzf backup${todayDate}.tgz --exclude=/proc --exclude=/lost+found --exclude=/backup${todayDate}.tgz --exclude=/mnt --exclude=/sys --exclude=/media /
还原系统
先从 u 盘 系统 启动
// 注意备份系统的时间
sudo tar xvpfz backup.tgz
创建被排除的目录
sudo mkdir proc lost+found mnt sys media
blkid /dev/sdb1
vi /etc/fstab
grub-install /dev/sdb
update-grub2